Meta-Stock: Task-Difficulty-Adaptive Meta-learning for Sub-new Stock Price Prediction

Sub-new stock price prediction, forecasting the price trends of stocks listed less than one year, is crucial for effective quantitative trading. While deep learning methods have demonstrated effectiveness in predicting old stock prices, they require large training datasets unavailable for sub-new stocks. In this paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learning approach for sub-new stock price prediction. Leveraging prediction tasks formulated by old stocks, our meta-learning method aims to acquire the fast generalization ability that can be further adapted to sub-new stock price prediction tasks, thereby solving the data scarcity of sub-new stocks. Moreover, we enhance the meta-learning process by incorporating an adaptive learning strategy sensitive to varying task difficulties. Through wavelet transform, we extract high-frequency coefficients to manifest stock price volatility. This allows the meta-learning model to assign gradient weights based on volatility-quantified task difficulty. Extensive experiments on datasets collected from three stock markets spanning twenty-two years prove that our Meta-Stock significantly outperforms previous methods and manifests strong applicability in real-world stock trading. Besides, we evaluate the reasonability of the task difficulty quantification and the effectiveness of the adaptive learning strategy

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images

In this paper, we aim to create generalizable and controllable neural signed distance fields (SDFs) that represent clothed humans from monocular depth observations. Recent advances in deep learning, especially neural implicit representations, have enabled human shape reconstruction and controllable avatar generation from different sensor inputs. However, to generate realistic cloth deformations from novel input poses, watertight meshes or dense full-body scans are usually needed as inputs. Furthermore, due to the difficulty of effectively modeling pose-dependent cloth deformations for diverse body shapes and cloth types, existing approaches resort to per-subject/cloth-type optimization from scratch, which is computationally expensive. In contrast, we propose an approach that can quickly generate realistic clothed human avatars, represented as controllable neural SDFs, given only monocular depth images. We achieve this by using meta-learning to learn an initialization of a hypernetwork that predicts the parameters of neural SDFs. The hypernetwork is conditioned on human poses and represents a clothed neural avatar that deforms non-rigidly according to the input poses. Meanwhile, it is meta-learned to effectively incorporate priors of diverse body shapes and cloth types and thus can be much faster to fine-tune, compared to models trained from scratch. We qualitatively and quantitatively show that our approach outperforms state-of-the-art approaches that require complete meshes as inputs while our approach requires only depth frames as inputs and runs orders of magnitudes faster. Furthermore, we demonstrate that our meta-learned hypernetwork is very robust, being the first to generate avatars with realistic dynamic cloth deformations given as few as 8 monocular depth frames.Comment: 17 pages, 9 figures. Project page: https://neuralbodies.github.io/metavatar

Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images

In medical applications, the same anatomical structures may be observed in multiple modalities despite the different image characteristics. Currently, most deep models for multimodal segmentation rely on paired registered images. However, multimodal paired registered images are difficult to obtain in many cases. Therefore, developing a model that can segment the target objects from different modalities with unpaired images is significant for many clinical applications. In this work, we propose a novel two-stream translation and segmentation unified attentional generative adversarial network (UAGAN), which can perform any-to-any image modality translation and segment the target objects simultaneously in the case where two or more modalities are available. The translation stream is used to capture modality-invariant features of the target anatomical structures. In addition, to focus on segmentation-related features, we add attentional blocks to extract valuable features from the translation stream. Experiments on three-modality brain tumor segmentation indicate that UAGAN outperforms the existing methods in most cases.Comment: 9 pages, 4 figures, Accepted by MICCAI201

Research on the Problem of Old-Age Care in China From the Perspective of Ethics

The elders are an important driving force for economic development, social progress and national prosperity. The care of the elderly, as a universal world problem, is related to their dignity, human rights and social stability. Since 2000, China has stepped into the threshold of an aging society. With the increasing aging population and the influence of population, economy, culture, politics and other uniquely national conditions, the conflicts and problems over providing for the aged are more complicated and severe. It has become an urgent issue in China how to solve a series of ethical dilemmas of providing for the aged caused by the aging population.Firstly, we use the data of the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) for positivist research, and then adopt the ordered Probit regression model for analysis. Secondly, we present and discuss the existing ethical problems of providing for the aged in society: firstly, the unfairness of allocation of resources in the society for providing for the aged; secondly, the dilution of concept of filial piety in family; thirdly, the weakening of the trend of respecting the elderly in society; last but not least, the lack of good in old-age security system. In view of the above-mentioned ethical problems of providing for the aged in China, we propose some solutions, namely, fairly distributing old-age resources in society; promoting the cultural tradition of filial piety; improving the system of endowment policy. Focusing on the ethical issues of old-age care, this paper provides some ethical thoughts for the solution of them

InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations

While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface. Such a dialogue system can help users explore datasets and models with explanations in a contextualized manner, e.g. via clarification or follow-up questions, and through a natural language interface. We adapt the conversational explanation framework TalkToModel (Slack et al., 2022) to the NLP domain, add new NLP-specific operations such as free-text rationalization, and illustrate its generalizability on three NLP tasks (dialogue act classification, question answering, hate speech detection). To recognize user queries for explanations, we evaluate fine-tuned and few-shot prompting models and implement a novel Adapter-based approach. We then conduct two user studies on (1) the perceived correctness and helpfulness of the dialogues, and (2) the simulatability, i.e. how objectively helpful dialogical explanations are for humans in figuring out the model's predicted label when it's not shown. We found rationalization and feature attribution were helpful in explaining the model behavior. Moreover, users could more reliably predict the model outcome based on an explanation dialogue rather than one-off explanations.Comment: EMNLP 2023 Findings. Camera-ready versio

Full-sky ray-tracing simulation of weak lensing using ELUCID simulations: exploring galaxy intrinsic alignment and cosmic shear correlations

The intrinsic alignment of galaxies is an important systematic effect in weak-lensing surveys, which can affect the derived cosmological parameters. One direct way to distinguish different alignment models and quantify their effects on the measurement is to produce mocked weak-lensing surveys. In this work, we use full-sky ray-tracing technique to produce mock images of galaxies from the ELUCID $N$-body simulation run with the WMAP9 cosmology. In our model we assume that the shape of central elliptical galaxy follows that of the dark matter halo, and spiral galaxy follows the halo spin. Using the mocked galaxy images, a combination of galaxy intrinsic shape and the gravitational shear, we compare the predicted tomographic shear correlations to the results of KiDS and DLS. It is found that our predictions stay between the KiDS and DLS results. We rule out a model in which the satellite galaxies are radially aligned with the center galaxy, otherwise the shear-correlations on small scales are too high. Most important, we find that although the intrinsic alignment of spiral galaxies is very weak, they induce a positive correlation between the gravitational shear signal and the intrinsic galaxy orientation (GI). This is because the spiral galaxy is tangentially aligned with the nearby large-scale overdensity, contrary to the radial alignment of elliptical galaxy. Our results explain the origin of detected positive GI term from the weak-lensing surveys. We conclude that in future analysis, the GI model must include the dependence on galaxy types in more detail.Comment: 23 pages, 13 figures, published in ApJ. Our mock galaxy catalog is available upon request by email to the author ([email protected], [email protected]

Kaposi’s Sarcoma-Associated Herpesvirus Reduces Cellular Myeloid Differentiation Primary-Response Gene 88 (MyD88) Expression via Modulation of Its RNA

Kaposi’s sarcoma (KS)-associated herpesvirus (KSHV) is a human gammaherpesvirus associated with several human malignancies. The replication and transcription activator (RTA) is necessary and sufficient for the switch from KSHV latency to lytic replication. Interleukin 1 (IL-1) is a major mediator for inflammation and plays an important role in both innate and adaptive immunity. Myeloid differentiation primary response gene 88 (MyD88) is an essential adaptor molecule for IL-1 as well as most Toll-like receptor signaling. In this study, we identified a novel mechanism by which KSHV interferes with host inflammation and immunity. KSHV RTA specifically reduces the steady-state protein levels of MyD88, and physiological levels of MyD88 are downregulated during KSHV lytic replication when RTA is expressed. The N-terminal region of RTA is required for the reduction of MyD88. Additional studies demonstrated that RTA targets MyD88 expression at the RNA level, inhibits RNA synthesis of MyD88, and may bind MyD88 RNA. Finally, RTA inhibits IL-1-mediated activation of NF-B. Because IL-1 is abundant in the KS microenvironment and inhibits KSHV replication, this work may expand our understanding of how KSHV evades host inflammation and immunity for its survival in vivo